Behavioral Economics and Aging David Laibson Harvard University and NBER July 8, 2009 RAND.

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Behavioral Economics and Aging David Laibson Harvard University and NBER July 8, 2009 RAND

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1. Motivating Experiments A Thought Experiment Would you like to have A)15 minute massage now or B) 20 minute massage in an hour Would you like to have C) 15 minute massage in a week or D) 20 minute massage in a week and an hour

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Read and van Leeuwen (1998) Time Choosing TodayEating Next Week If you were deciding today, would you choose fruit or chocolate for next week?

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Read, Loewenstein & Kalyanaraman (1999) Choose among 24 movie videos Some are “low brow”: Four Weddings and a Funeral Some are “high brow”: Schindler’s List Picking for tonight: 66% of subjects choose low brow. Picking for next Wednesday: 37% choose low brow. Picking for second Wednesday: 29% choose low brow. Tonight I want to have fun… next week I want things that are good for me.

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Extremely thirsty subjects McClure, Ericson, Laibson, Loewenstein and Cohen (2007) Choosing between, juice now or 2x juice in 5 minutes 60% of subjects choose first option. Choosing between juice in 20 minutes or 2x juice in 25 minutes 30% of subjects choose first option. We estimate that the 5-minute discount rate is 50% and the “long-run” discount rate is 0%. Ramsey (1930s), Strotz (1950s), & Herrnstein (1960s) were the first to understand that discount rates are higher in the short run than in the long run.

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Conceptual Outline People are not internally consistent decision-makers Internal conflicts can be modeled and measured Early understanding of the neural foundations Scalable, inexpensive policies can transform behavior

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Outline 1.Motivating experimental evidence 2.Theoretical framework 3.Field evidence 4.Neuroscience foundations 5.Neuroimaging evidence 6.Policy discussion 7. The age of reason A copy of these slides will soon be available on my Harvard website.

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Rapid rate of decline in short run Slow rate of decline in long run

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An exponential discounting paradox. Suppose people discount at least 1% between today and tomorrow. Suppose their discount functions were exponential. Then 100 utils in t years are worth 100*e (-0.01)*365*t utils today. What is 100 today worth today? 100.00 What is 100 in a year worth today? 2.55 What is 100 in two years worth today? 0.07 What is 100 in three years worth today? 0.00

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Kaur, Kremer, and Mullainathan (2009): Compare two piece-rate contracts: 1.Linear piece-rate contract (“Control contract”) –Earn w per unit produced 2.Linear piece-rate contract with penalty if worker does not achieve production target T (“Commitment contract”) –Earn w for each unit produced if production>=T, earn w/2 for each unit produced if production
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Kaur, Kremer, and Mullainathan (2009): Demand for Commitment (non-paydays) –Commitment contract (Target>0) chosen 39% of the time –Workers are 11 percentage points more likely to choose commitment contract the evening before Effect on Production (non-paydays) –Being offered contract choice increases average production by 5 percentage points relative to control –Implies 13 percentage point productivity increase for those that actually take up commitment contract –No effects on quality of output (accuracy) Payday Effects (behavior on paydays) –Workers 21 percentage points more likely to choose commitment (Target>0) morning of payday –Production is 5 percentage points higher on paydays

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4. Neuroscience Foundations What is the underlying mechanism? Why are our preferences inconsistent? Is it adaptive? How should it be modeled? Does it arise from a single time preference mechanism (e.g., Herrnstein’s reward per unit time)? Or is it the resulting of multiple systems interacting (Shefrin and Thaler 1981, Bernheim and Rangel 2004, O’Donoghue and Loewenstein 2004, Fudenberg and Levine 2004)?

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Shiv and Fedorikhin (1999) Cognitive burden/load is manipulated by having subjects keep a 2-digit or 7-digit number in mind as they walk from one room to another On the way, subjects are given a choice between a piece of cake or a fruit-salad Processing burden% choosing cake Low (remember only 2 digits)41% High (remember 7 digits)63%

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Hypothesize that the fronto-parietal system is patient Hypothesize that mesolimbic system is impatient. Then integrated preferences are quasi-hyperbolic Relationship to quasi-hyperbolic model nowt+1t+2t+3 PFC1111… Mesolimbic1000… Total2111… Total normed11/2 …

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Conclusions of Amazon study Time discounting results from the combined influence of two neural systems: Mesolimbic dopamine system is impatient. Fronto-parietal system is patient. These two systems are separately implicated in ‘emotional’ and ‘analytic’ brain processes. When subjects select delayed rewards over immediately available alternatives, analytic cortical areas show enhanced changes in activity.

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Open questions  New experiment on primary rewards: Juice McClure, Ericson, Laibson, Loewenstein, Cohen (Journal of Neuroscience, 2007) 1.What is now and what is later? Our “immediate” option (Amazon gift certificate) did not generate immediate “consumption.” Also, we did not control the time of consumption. 2.How does the limbic signal decay as rewards are delayed? 3.Would our results replicate with a different reward domain? 4.Would our results replicate over a different time horizon?

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Subjects water deprived for 3hr prior to experiment (subject scheduled for 6:00)

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Measuring discount functions using neuroimaging data Impatient voxels are in the emotional (mesolimbic) reward system Patient voxels are in the analytic (prefrontal and parietal) cortex Average (exponential) discount rate in the impatient regions is 4% per minute. Average (exponential) discount rate in the patient regions is 1% per minute.

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More activity in DLPFC in trials with successful self control than in trials with unsuccessful self-control L  p
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Summary of neuroimaging evidence One system associated with midbrain dopamine neurons (mesolimbic dopamine system) discounts at a high rate. Second system associated with lateral prefrontal and posterior parietal cortex responsible for self- regulation (and shows relatively little discounting) Combined function of these two systems accounts for decision making across choice domains, including non-exponential discounting regularities.

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6. Policy Defaults in the savings domain Welcome to the company If you don’t do anything – You are automatically enrolled in the 401(k) – You save 2% of your pay – Your contributions go into a default fund Call this phone number to opt out of enrollment or change your investment allocations

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Participants stay at the automatic enrollment defaults for a long time. Fraction of Participants Hired Under Automatic Enrollment who are still at both Default Contribution Rate and Asset Allocation Company B Company C Company D Fraction of Participants Tenure at Company (Months)

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The power of deadlines: Active decisions Carroll, Choi, Laibson, Madrian, Metrick (2004) Active decision mechanisms require employees to make an active choice about 401(k) participation. Welcome to the company You are required to submit this form within 30 days of hire, regardless of your 401(k) participation choice If you don’t want to participate, indicate that decision If you want to participate, indicate your contribution rate and asset allocation Being passive is not an option

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Use automaticity and deadlines to nudge people to make better health decisions One early example: Home delivery of chronic meds (e.g. maintenance drugs for diabetes and CVD) Pharmaceutical adherence is about 50% One problem: need to pick up your meds Idea: use active decision intervention to encourage workers on chronic meds to consider home delivery Early results: HD take up rises from 14% to 38% Extensions to health domain

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$100 bills on the sidewalk Choi, Laibson, Madrian (2004) Employer 401(k) match is an instantaneous, riskless return Particularly appealing if you are over 59½ years old – Can withdraw money from 401(k) without penalty On average, half of employees over 59½ years old are not fully exploiting their employer match Educational intervention has no effect

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What is the Channel for the Age Effect? Banks offer different APRs when the loan-to- value (LTV) ratio is: –less than 80 percent –between 80 and 90 percent –over 90 percent Borrowers estimate their LTV by estimating their house value Banks form their own LTV estimates “Rate-Changing Mistake”: when borrower and bank LTVs straddle two of these categories –E.g., borrower LTV 80.

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Rate Changing Mistakes generate two sources of disadvantage for the customer: –If I underestimate my LTV (Loan-to-Value ratio), the bank can penalize me by deviating from its normal offer sheet. –If I overestimate my LTV (i.e., underestimate the value of my house), the bank will penalize me by not correcting my mistake and allowing me to borrow at too high a rate.

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A Rate-Changing Mistake costs 125 to 150 basis points. Next slides plot: –Rate-Changing Mistakes by age –APRs for borrowers who do NOT make a Rate-Changing Mistake

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For consumers who don’t make a Rate- Changing Mistake, age effect is small All the action is due to consumers who make a Rate-Changing Mistake –That is, consumers who over- or under-estimate their house values (relative to bank model) The propensity to make the mistake is U- shaped with age Hence, the final APR is U-shaped with age

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Two channels by which RCM raise interest payments Direct channel: old and young borrowers may have a higher ex-ante likelihood of making a RCM Indirect channel: old and young borrowers may have a higher ex-poste likelihood of accepting the high interest rates they receive after they make a RCM (instead of shopping around)

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(3) “Eureka”: Learning to Avoid Interest Charges on Balance Transfer Offers Balance transfer offers: borrowers pay lower APRs on balances transferred from other cards for a six-to- nine-month period New purchases on card have higher APRs Payments go towards balance transferred first, then towards new purchases Optimal strategy: make no new purchases on card to which balance has been transferred

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Eureka: Predictions Borrowers may not initially understand / be informed about card terms Borrowers may learn about terms by observing interest charges on purchases, or talking to friends –We should see “eureka” moments: new purchases on balance-transfer cards should drop to zero (in the month after borrowers “figure out” the card terms) Study: 14,798 accounts which accepted such offers over the period January 2000 to December 2002